IMPACTS OF COLD WEATHER ON HEALTH IN TEXAS

Recent events with recorded low temperature and unusual snow accumulation in the United States and Europe have raised the public awareness of the potential health impacts of extreme winter weather. Excessive cold was the leading cause of weather-related death during 2006-2010 in the U.S., accounting for 63% of weather related deaths. Several studies worldwide have demonstrated that, in general, mortality rates are higher in winter compared to summer. Studies have also shown that the association between cold temperature and death vary across cities, regions and countries and is especially relevant with decreasing latitude or in regions with mild winter climate. In addition to cold temperatures, higher mortality rates may be attributable to cold wave, an extended period of extreme cold temperature. However, due to global climate change, attention has focused on current and future heat waves on human health rather than cold waves. Despite the fact that climate change is expected to increase the intensity of winter storms, only a few studies have investigated cold wave-mortality association. Further, the results of these studies are inconsistent. In addition, most studies have focused on all-cause and cause-specific mortality, cold-related morbidity was less studied. The long-term goal of this study is to improve the understanding of how cold temperature and cold wave affect human health and to reduce adverse health effects of future cold events. The dissertation used time-series data with Poisson regression model to quantify both cold temperature effect and cold wave effect in Texas, one of the most populous and largest states that covers a variety of demographical and geographical feature with a general mild winter climate as located in the southern USA. Daily counts of deaths/emergency hospital admissions were modeled with both temperature and different cold-wave definitions for 12 major Metropolitan Areas (MSAs). Moreover, considering winter weather patterns are anticipated to become more variable with increasing average global temperatures, we used downscaled global climate models with population projection to estimate future public health burden attributable to cold temperature. The study showed that cold weather generally increases health risk significantly in Texas ranging from 0.1% to 5.0% for mortality and 0.1% to 3.8% for emergency hospital admissions with a 1⁰C decrease in temperature below the cold thresholds. The cold effects vary with age groups with highest risk in people over 75-year old. The strongest cold effects were associated with mortality in heart diseases and with emergency hospital admission in respiratory diseases. We found although the annual cold- mortality rates reduced with projected temperature under climate change, the number of deaths attributable to cold temperature increased largely with projected population through the end of the century. The findings can improve the understanding of cold-related health impacts in southern U.S. regions, and help local governments allocate resources to the areas in greatest need. This study can provide evidence for local policy makers to design strategies in reducing future public health burden of temperature-related deaths

RoboGen: Towards Unleashing Infinite Data for Automated Robot Learning via Generative Simulation

We present RoboGen, a generative robotic agent that automatically learns diverse robotic skills at scale via generative simulation. RoboGen leverages the latest advancements in foundation and generative models. Instead of directly using or adapting these models to produce policies or low-level actions, we advocate for a generative scheme, which uses these models to automatically generate diversified tasks, scenes, and training supervisions, thereby scaling up robotic skill learning with minimal human supervision. Our approach equips a robotic agent with a self-guided propose-generate-learn cycle: the agent first proposes interesting tasks and skills to develop, and then generates corresponding simulation environments by populating pertinent objects and assets with proper spatial configurations. Afterwards, the agent decomposes the proposed high-level task into sub-tasks, selects the optimal learning approach (reinforcement learning, motion planning, or trajectory optimization), generates required training supervision, and then learns policies to acquire the proposed skill. Our work attempts to extract the extensive and versatile knowledge embedded in large-scale models and transfer them to the field of robotics. Our fully generative pipeline can be queried repeatedly, producing an endless stream of skill demonstrations associated with diverse tasks and environments

Applying Factor Analysis Combined with Kriging and Information Entropy Theory for Mapping and Evaluating the Stability of Groundwater Quality Variation in Taiwan

In Taiwan many factors, whether geological parent materials, human activities, and climate change, can affect the groundwater quality and its stability. This work combines factor analysis and kriging with information entropy theory to interpret the stability of groundwater quality variation in Taiwan between 2005 and 2007. Groundwater quality demonstrated apparent differences between the northern and southern areas of Taiwan when divided by the Wu River. Approximately 52% of the monitoring wells in southern Taiwan suffered from progressing seawater intrusion, causing unstable groundwater quality. Industrial and livestock wastewaters also polluted 59.6% of the monitoring wells, resulting in elevated EC and TOC concentrations in the groundwater. In northern Taiwan, domestic wastewaters polluted city groundwater, resulting in higher NH3-N concentration and groundwater quality instability was apparent among 10.3% of the monitoring wells. The method proposed in this study for analyzing groundwater quality inspects common stability factors, identifies potential areas influenced by common factors, and assists in elevating and reinforcing information in support of an overall groundwater management strategy

Robust estimation of bacterial cell count from optical density

Optical density (OD) is widely used to estimate the density of cells in liquid culture, but cannot be compared between instruments without a standardized calibration protocol and is challenging to relate to actual cell count. We address this with an interlaboratory study comparing three simple, low-cost, and highly accessible OD calibration protocols across 244 laboratories, applied to eight strains of constitutive GFP-expressing E. coli. Based on our results, we recommend calibrating OD to estimated cell count using serial dilution of silica microspheres, which produces highly precise calibration (95.5% of residuals <1.2-fold), is easily assessed for quality control, also assesses instrument effective linear range, and can be combined with fluorescence calibration to obtain units of Molecules of Equivalent Fluorescein (MEFL) per cell, allowing direct comparison and data fusion with flow cytometry measurements: in our study, fluorescence per cell measurements showed only a 1.07-fold mean difference between plate reader and flow cytometry data

Measuring the Complex Permittivities of Plastics in Irregular Shapes

This work presents the measurement of the complex permittivities of high density polyethylene (HDPE), linear low density polyethylene (LLDPE), low density polyethylene (LDPE), polypropylene (PP), Nylon, and thermoplastic vulcanizates (TPV) in irregular shapes at the microwave frequency. A Teflon sample holder was employed to pack irregularly shaped plastic materials with various volumetric percentages. The samples were put into a resonant cavity with an enhanced electric field in its center, which is known as the enhanced-field method (EFM). The resonant frequencies and the quality factors at different volumetric percentages were measured by a network analyzer and compared with simulated results using a full-wave simulator (high-frequency structure simulator (HFSS)). Three simulation models, layer, ring, and hybrid, are proposed and compared with the experimental results. It is found that the hybrid model (denoted as Z5R5) with five heights and five radii in the partition is the most suitable. The complex permittivities of six plastic materials were evaluated by the contour maps of the HFSS simulation using the hybrid model. The measured complex permittivities of the irregularly shaped polymers agree well with their counterparts in bulk form

Impact of thermal-induced sapphire substrate erosion on material and photodetector characteristics of sputtered Ga2O3 films

Monoclinic β–phase gallium oxide (β-Ga2O3) films were deposited on c-plane sapphire substrates using a combination of sputtering and post annealing processes. The effect of thermal-induced sapphire substrate erosion on material characteristics of sputtered β-Ga2O3 film has been investigated. Both the furnace and rapid thermal annealing (RTA) were performed in the air ambient for the post thermal treatments. After the annealing process, all the deposited films transformed from amorphous to monoclinic crystalline structure with excellent transmittance above 80% in the visible region. Meanwhile, a thermal-induced interdiffusion phenomenon has been observed in β-Ga2O3/sapphire architecture, particularly for furnace-annealed samples. Even though high-temperature post thermal treatments can enhance the crystallinity of the Ga2O3 films continuously, a degraded photodetector performance is observed for the samples annealed above 800 °C due to the thermal-induced aluminum (Al) diffusion issue. The interdiffusion mechanism for the sputtered Ga2O3-on-sapphire films is proposed and its effects on material and photodetector characteristics are elucidated. An optimum metal-semiconductor-metal photodetector performance is achieved for the 800°C-RTA-treated Ga2O3 sample with the photo/dark current ratio of 1.78 × 105 and responsivity of 0.553 A/W (at 5 V bias)